This guide is built for senior operators who need to act fast in 2026. Each chapter ends with a "what to do this week" callout, and every claim is sourced -- internal Dcrayon engagement data, named vendor reports, or industry benchmarks. No fluff, no vendor promo.
Table of contents
- 1. What is llms.txt and why publishers are adopting it
- 2. llms.txt vs llms-full.txt: the spec
- 3. Writing a high-signal llms.txt for your site
- 4. Schema markup that helps LLMs
- 5. Schema markup that gets ignored (or hurts)
- 6. Robots directives for AI crawlers: GPTBot, ClaudeBot, Google-Extended, PerplexityBot
- 7. Server logs: tracking which AI is crawling you
- 8. The Dcrayon technical AI visibility checklist
- 9. About Dcrayon + next step
Who this is for
Founders, CMOs, VPs of Marketing, agency leads, and senior operators working on programs scoped above Rs 25 lakh per quarter. If you are evaluating Dcrayon for a 2026 program, this is the reference document for the conversation.
How to use it
Skim the table of contents above. Jump to the chapter most relevant to the gap you are working on. Bring screenshots, raw numbers, or your last Dcrayon Score readout to the scoping call -- this guide will not replace the conversation, but it will sharpen the questions you bring.
llms.txt and Schema for LLMs: Technical AI Visibility ships as a designed PDF with diagrams and worksheets. Request the download via the form on the right; we email it inside one business day.
